


import warnings
from pathlib import Path
from typing import List, Tuple, Union

import fire
from torch import nn

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging


__UpperCamelCase     		=							logging.get_logger(__name__)
def 							UpperCamelCase_(					_A :nn.ModuleList					,  _A :nn.ModuleList					,  _A :List[int]	)->		None:
			UpperCamelCase__       =							nn.ModuleList([src_layers[i] for i in layers_to_copy]	)
			assert len(_A	) == len(_A	), F'''{len(_A	)} != {len(_A	)}'''
			dest_layers.load_state_dict(layers_to_copy.state_dict()	)


__UpperCamelCase     		=							{
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    1_2: {
        1: [0],  # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
        2: [0, 6],
        3: [0, 6, 1_1],
        4: [0, 4, 8, 1_1],
        6: [0, 2, 4, 7, 9, 1_1],
        9: [0, 1, 2, 4, 5, 7, 9, 1_0, 1_1],
        1_2: list(range(1_2)),
    },
    1_6: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 1_5],
        3: [0, 8, 1_5],
        4: [0, 5, 1_0, 1_5],
        6: [0, 3, 6, 9, 1_2, 1_5],
        8: [0, 2, 4, 6, 8, 1_0, 1_2, 1_5],
        9: [0, 1, 3, 5, 7, 9, 1_1, 1_3, 1_5],
        1_2: [0, 1, 2, 3, 4, 5, 6, 7, 9, 1_1, 1_3, 1_5],
        1_6: list(range(1_6)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
__UpperCamelCase     		=							{
    # maps  num layers in student -> which teacher layers to copy.
    6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
    1_2: {1: [1_1], 2: [5, 1_1], 3: [3, 7, 1_1], 6: [1, 3, 5, 8, 1_0, 1_1]},
    1_6: {1: [1_5], 4: [4, 9, 1_2, 1_5], 8: [1, 3, 5, 7, 9, 1_1, 1_3, 1_5]},
}
def 							UpperCamelCase_(					_A :Union[str, Any]					,  _A :List[Any]	)->		Optional[int]:
			try:
						UpperCamelCase__       =							LAYERS_TO_COPY[n_teacher][n_student]
						return val
			except KeyError:
						if n_student != n_teacher:
									warnings.warn(
									    F'''no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first'''
									    F''' {n_student}'''	)
						return list(range(_A	)	)
def 							UpperCamelCase_(					_A :Any					,  _A :str	)->		List[int]:
			if n_student > n_teacher:
						raise ValueError(F'''Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}'''	)
			elif n_teacher == n_student:
						return list(range(_A	)	)
			elif n_student == 1:
						return [n_teacher - 1]
			else:
						return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def 							UpperCamelCase_(					_A :Union[str, PreTrainedModel]					,  _A :Union[str, Path] = "student"					,  _A :Union[int, None] = None					,  _A :Union[int, None] = None					,  _A :Union[str, Any]=False					,  _A :Optional[int]=None					,  _A :Any=None					,  **_A :Any					,  )->		Tuple[PreTrainedModel, List[int], List[int]]:
			UpperCamelCase__       =							"encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."
			assert (e is not None) or (d is not None), _msg
			if isinstance(_A					,  _A	):
						AutoTokenizer.from_pretrained(_A	).save_pretrained(_A	)  # purely for convenience
						UpperCamelCase__       =							AutoModelForSeqaSeqLM.from_pretrained(_A	).eval()
			else:
						assert isinstance(_A					,  _A	), F'''teacher must be a model or string got type {type(_A	)}'''
			UpperCamelCase__       =							teacher.config.to_diff_dict()

			try:
						UpperCamelCase__,   UpperCamelCase__       =							teacher.config.encoder_layers, teacher.config.decoder_layers
						if e is None:
									UpperCamelCase__       =							teacher_e
						if d is None:
									UpperCamelCase__       =							teacher_d
						init_kwargs.update({"encoder_layers": e, "decoder_layers": d}	)
			except AttributeError:  # T5
						if hasattr(teacher.config					,  "num_encoder_layers"	):
									UpperCamelCase__,   UpperCamelCase__       =							teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
						else:
									UpperCamelCase__,   UpperCamelCase__       =							teacher.config.num_layers, teacher.config.num_decoder_layers
						if e is None:
									UpperCamelCase__       =							teacher_e
						if d is None:
									UpperCamelCase__       =							teacher_d
						if hasattr(teacher.config					,  "num_encoder_layers"	):
									init_kwargs.update({"num_encoder_layers": e, "num_decoder_layers": d}	)
						else:
									init_kwargs.update({"num_layers": e, "num_decoder_layers": d}	)

    # Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
			init_kwargs.update(_A	)

			# Copy weights
			UpperCamelCase__       =							teacher.config_class(**_A	)
			UpperCamelCase__       =							AutoModelForSeqaSeqLM.from_config(_A	)
			# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
			UpperCamelCase__       =							student.load_state_dict(teacher.state_dict()					,  strict=_A	)
			assert info.missing_keys == [], info.missing_keys  # every student key should have a teacher keys.

			if copy_first_teacher_layers:  # Our copying is done. We just log and save
						UpperCamelCase__,   UpperCamelCase__       =							list(range(_A	)	), list(range(_A	)	)
						logger.info(
						    F'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to'''
						    F''' {save_path}'''	)
						student.save_pretrained(_A	)
						return student, e_layers_to_copy, d_layers_to_copy

			# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
			if e_layers_to_copy is None:
						UpperCamelCase__       =							pick_layers_to_copy(_A					,  _A	)
			if d_layers_to_copy is None:
						UpperCamelCase__       =							pick_layers_to_copy(_A					,  _A	)

			try:
						if hasattr(
						    _A					,  "prophetnet"	):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
									copy_layers(teacher.prophetnet.encoder.layers					,  student.prophetnet.encoder.layers					,  _A	)
									copy_layers(teacher.prophetnet.decoder.layers					,  student.prophetnet.decoder.layers					,  _A	)
						else:
									copy_layers(teacher.model.encoder.layers					,  student.model.encoder.layers					,  _A	)
									copy_layers(teacher.model.decoder.layers					,  student.model.decoder.layers					,  _A	)
			except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
						copy_layers(teacher.encoder.block					,  student.encoder.block					,  _A	)
						copy_layers(teacher.decoder.block					,  student.decoder.block					,  _A	)
			logger.info(
			    F'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}'''	)
			UpperCamelCase__       =							{
			    "teacher_type": teacher.config.model_type,
			    "copied_encoder_layers": e_layers_to_copy,
			    "copied_decoder_layers": d_layers_to_copy,
			}
			student.save_pretrained(_A	)
			# Save information about copying for easier reproducibility

			return student, e_layers_to_copy, d_layers_to_copy


if __name__ == "__main__":
						fire.Fire(create_student_by_copying_alternating_layers)






from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
						from .pipeline_kandinsky import KandinskyPipeline
						from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
						from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
						from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
						from .text_encoder import MultilingualCLIP


